Media data modification management system

ABSTRACT

A processor may manage media modification within a participant network. A processor may generate the participant network having a participant. The participant may be associated with one or more media data. A processor may enforce one or more rules on the participant network using an artificial intelligence (AI) based governance system. The one or more rules may manage the modification of the one or more media data by the participant. A processor may restrict the participant from modifying the one or more media data based, at least in part, on the AI based governance system.

BACKGROUND

The present disclosure relates generally to the field of syntheticmedia, and more particularly to the modification of media data.

Media data may include any media such as video, images, audio, and/ormultimedia. Synthetic media may include any media data which has beenmodified, either by a person (e.g., using computer software) and/orartificial intelligence (AI). While various industries, such as theentertainment and marketing industries, may benefit from generatingsynthetic media based off of the modification of media data, thegeneration of synthetic media may also be used for nefarious purposes.

SUMMARY

Embodiments of the present disclosure include a method, computer programproduct, and system for managing media modification within a participantnetwork. A processor may generate the participant network having aparticipant. The participant may be associated with one or more mediadata. A processor may enforce one or more rules on the participantnetwork using an artificial intelligence (AI) based governance system.The one or more rules may manage the modification of the one or moremedia data by the participant. A processor may restrict the participantfrom modifying the one or more media data based, at least in part, onthe AI based governance system.

The above summary is not intended to describe each illustratedembodiment or every implementation of the present disclosure

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings included in the present disclosure are incorporated into,and form part of, the specification. They illustrate embodiments of thepresent disclosure and, along with the description, serve to explain theprinciples of the disclosure. The drawings are only illustrative ofcertain embodiments and do not limit the disclosure.

FIG. 1 depicts a block diagram of an embodiment of an media modificationmanagement system, in accordance with the present disclosure.

FIG. 2 illustrates a flowchart of a method for managing mediamodification, in accordance with embodiments of the present disclosure.

FIG. 3A illustrates a cloud computing environment, in accordance withembodiments of the present disclosure.

FIG. 3B illustrates abstraction model layers, in accordance withembodiments of the present disclosure.

FIG. 4 illustrates a high-level block diagram of an example computersystem that may be used in implementing one or more of the methods,tools, and modules, and any related functions, described herein, inaccordance with embodiments of the present disclosure.

While the embodiments described herein are amenable to variousmodifications and alternative forms, specifics thereof have been shownby way of example in the drawings and will be described in detail. Itshould be understood, however, that the particular embodiments describedare not to be taken in a limiting sense. On the contrary, the intentionis to cover all modifications, equivalents, and alternatives fallingwithin the spirit and scope of the disclosure.

DETAILED DESCRIPTION

Aspects of the present disclosure relate generally to the field ofsynthetic media, and more particularly to the modification of mediadata. While the present disclosure is not necessarily limited to suchapplications, various aspects of the disclosure may be appreciatedthrough a discussion of several examples using this context.

Media data may include any media format such as text (e.g.,spreadsheets), video, images, audio, and/or multimedia. Synthetic mediamay include any media data which has been modified (e.g., altered,modified, transformed, etc.) from either its original form or anotherderivative of the original form. For example, a person may capture avideo of their dog and upload the video to a software application thatallows them to alter/modify the video (e.g., from the video's originalform) to include audio (e.g., synthetic form). In this example when thevideo (e.g., synthetic media) is played, the dog may look like it isspeaking. Continuing this example, the person could send this video to afriend who may alter the video further by modifying the video so the dognow looks like it is wearing a hat. In this example, the friend would bealtering a derivative of the original form (e.g., already consideredsynthetic media) to form the synthetic media. While this exampledemonstrates a lighthearted side to synthetic media, advances inmodification techniques have led to more nefarious synthetic media.

Developments in artificial intelligence (AI) and machine learning haveenabled the production of high quality modified media data. Whilehistorical modification techniques have traditionally been identified bya person or through analysis as synthetic media, the production of highquality modified media data may use various AI techniques to alter theoriginal media data in such a way as to confuse the public on whatversion is the original version. One such category of high qualitymodified media data are known as deep fakes. While there are positiveconnotations associated with deepfakes, they traditionally include themodification of an image or video to generate a false interpretation ofthe image or video. For example, a person may be captured on videopresenting a nonconfrontational topic, but using AI and machine learningtechniques (e.g., associated with music synthesis, text generation,human image/video synthesis, speech synthesis, etc.) the video may bemodified to show the person presenting an offensive or confrontationaltopic. Such high quality media data modification often has the potentialto deceive audiences and lead to confusion regarding what is true orfalse.

While efforts are being made to detect deepfakes or other types ofsynthetic media using AI, such detections are difficult because of thecyclic nature of AI. For example, when AI identifies an indicator ofsynthetic media it also learns what aspect of the synthetic media needsto be further modified in order to be convincing. As such, there is adesire for standards that may identify when media data has beenmodified, who has produced the modified media data, and restricting thegeneration of modified media data (e.g., synthetic media).

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the disclosure.As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, steps, operations, elements, components, and/or groupsthereof.

It will be readily understood that the instant components, as generallydescribed and illustrated in the figures herein, may be arranged anddesigned in a wide variety of different configurations. Accordingly, thefollowing detailed description of the embodiments of at least one of amethod, apparatus, non-transitory computer readable medium and system,as represented in the attached figures, is not intended to limit thescope of the application as claimed but is merely representative ofselected embodiments.

The instant features, structures, or characteristics as describedthroughout this specification may be combined or removed in any suitablemanner in one or more embodiments. For example, the usage of the phrases“example embodiments,” “some embodiments,” or other similar language,throughout this specification refers to the fact that a particularfeature, structure, or characteristic described in connection with theembodiment may be included in at least one embodiment. Accordingly,appearances of the phrases “example embodiments,” “in some embodiments,”“in other embodiments,” or other similar language, throughout thisspecification do not necessarily all refer to the same group ofembodiments, and the described features, structures, or characteristicsmay be combined or removed in any suitable manner in one or moreembodiments. Further, in the FIGS., any connection between elements canpermit one-way and/or two-way communication even if the depictedconnection is a one-way or two-way arrow.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present disclosure has been presented for purposes ofillustration and description but is not intended to be exhaustive orlimited to the disclosure in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiment was chosen and described in order to best explain theprinciples of the disclosure and the practical application and to enableothers of ordinary skill in the art to understand the disclosure forvarious embodiments with various modifications as are suited to theparticular use contemplated.

In embodiments discussed herein, solutions are provided in the form of amethod, system, and computer program product, for managing media datamodification within a participant network. Embodiments as contemplatedherein, enable a participant or a set of participants of the participantnetwork to manage the modification of media data based, at least inpart, on a governance system. The governance system may enable theparticipant or a set of participants of a participant network to managehow media data may be modified. In embodiments, a governance system maybe driven by an AI engine and may include one or more rules thatregulate the modification of media data. While in some embodiments, theone or more rules may be applied equally to all of the participants ofthe participant network, in other embodiments, one or more rules may beapplied disparately among participants of the participant network. Inembodiments, a processor may generate the one or more rules of thegovernance system in a variety of ways including, but not limited tousing an AI engine to autogenerate the rules or a participant or a setof participants (e.g., owner(s) of the media data) may manually generatethe rules. The participant network contemplated herein can be customizedto meet different participant or a set of participants needs.

In embodiments, a processor may generate a participant network among anynumber of participants. Participants may contribute to any processassociated with the lifecycle of media data modification. For example, aparticipant may generate media data (e.g., taking a video with a mobilephone) or may modify data in some form (e.g., create a deepfake). Inembodiments, participants who generate media data may be consideredowners of the media data (e.g., hold the copyright to the media data).In some embodiments, a processor may also allow participants to purchaseand/or license media data.

In embodiments, a processor may configure the participant network withina blockchain network (e.g., Hyperledger Fabric). In some embodiments, aprocessor may use the blockchain network to trace one or more actions(e.g., modification, editing, etc.) associated with the participantnetwork. The blockchain may provide a ledger of the one or more actionsof the participant network and may act as an immutable database thatmaintains an accounting of each participant and particular media dataassociated with the participant network. For example, each time aparticipant generates a media data, either through creating/capturingthe media data or creating a derivative media data using modificationtechniques, metadata may be collected and automatically incorporatedwithin the blockchain. This metadata may include, but is not limited toparticipant details (e.g., username, email, etc.), device details (e.g.,device type, device identification, etc.), media data details (e.g., GPSlocation of picture taken), and what participants have viewed theparticular media data.

In one example embodiment, a participant may capture or create anoriginal image (e.g., using either a camera or digital paintingsoftware). Metadata may be collected regarding the device and/orsoftware used to capture/create the picture, the participant's ownershipof the original picture, participant details, and information associatedwith the original picture. In this example embodiment, the participantmay decide to alter (e.g., modify) the picture from its original form bymodifying the picture to include a new superimposed background. Inembodiments, a processor may collect additional metadata associated withthis new modified version of the media data and incorporate theadditional metadata into the blockchain. This additional metadata couldinclude details associated with the participant that performed themodification(s), details associated with the modifications and/or amodification category (e.g., simple, medium, or complex modification),and details associated with what devices and/or software/applicationsthat may have been used to perform the modifications.

In some embodiments, a processor may collect additional metadata when aparticipant purchases, licenses a particular media data, or downloads aparticular media data (e.g., associated with the participant network).The metadata and/or additional metadata that may be generated each timea particular media data is modified and/or each time a participant gainsaccess to a particular media data. For example, a participant may gainaccess to media data through gaining some form of ownership rights. Insome embodiments, once the metadata and/or additional metadata isincorporated into the blockchain, some or all of the metadata/additionalmetadata may be accessed by some or all of the participants of theparticipant network. Utilizing blockchain within the participant networkcreates an immutable ledger that may record information associated notonly with the transition of ownership rights, but also a clear record ofwhat participants performed the particular modifications. This may allowparticipants to observe how a particular media data has been modifiedfrom its original form.

In embodiments, a processor may generate a governance system to managethe modification of media data by enforcing one or more rules on theparticipants of the participant network. In these embodiments, aprocessor may configure an AI engine to perform various enforcementfunctions of the governance system. In embodiments, the AI engine may beconfigured to use various AI techniques and machine learning frameworks(e.g., autoencoders and generative adversarial networks) to perform thevarious functions of the governance system. These functions include, butare not limited to, the management/regulation of media data, security ofmedia data, and authenticity of media data. In embodiments, a processormay configure the AI engine to analyze a modification of the one or moremedia data performed by a participant. In these embodiments, using theAI engine, a processor may determine if the modification performed onthe media data violates one or more rules. In embodiments, one or morerules may be generated using various methods including, but not limitedto, an AI rule engine (see below), a participant may manually define oneor more rules associated with media data that they own or have ownershiprights to, or any combination thereof.

A governance system may include, but is not limited to, rules that applyto every participant of the participant network, rules that apply toparticular categories of participants, rules that are associated with aspecific participant, or any combination thereof. In one exampleembodiment, a governance system may include a rule that applies to everyparticipant stipulating that a participant must have valid permissionfrom the owner of the particular media data prior to the participantperforming modifications on a particular media data. In another exampleembodiment, a governance system may include a rule associated with aparticular media data restricting the types of modification aparticipant may perform on the media data. For example, a participantmay be allowed to modify a video by adding different filters to thevideo images, but may not be allowed to modify the video my altering theaudio associated with the video. In some embodiments, a processor mayprovide an owner exception to a modification that violates the one ormore rules. For example, in embodiments where a processor a analyzes amodification of a media data performed by a participant and determinesthe modification violates one or more rules, a processor may identifythe participant as the owner of the media data. In these embodiments,where the participant violating the one or more rules is the owner ofthe media data, a processor may bypass the one or more rules and allowthe owner to perform the modification.

In many embodiments, as contemplated herein, the enforcement of the oneor more rules associated with a particular media data can only bemanaged if the media data is maintained within the participant network.As such, another example of one or more rules may include preventing themedia data or an unsanctioned export (e.g., without the owner'sapproval) of the particular media data from the participant network. Forexample, in some embodiments, the AI engine may be able to detect if aparticipant is capturing a screen shot or screen recording of the mediadata or if a participant is attempting to remove the media data into acloud storage device (e.g., external system) not recognized by theparticipant network. In these embodiments, the processor may preventsuch actions and issue a warning message (e.g., alert notification).

In some embodiments, a processor may analyze one or more attemptedtransfers of media data. In these embodiments, a processor may analyzeif a participant is attempting to use a messaging application, email,memory cards, Bluetooth, airdrop, and/or any other method oftransferring media data using hardware and/or software/applications. Inembodiments, a processor may restrict some or all of the aforementionedtransfers based, at least in part, on if the transfer and/or transfertype violates the one or more rules. In these embodiments, if aprocessor determines that one or more rules have been violated eitherbased on the transfer or particular type of transfer, a processor mayissue an alert notification and prevent the transfer from occurring. Insome embodiments, a processor may prevent the transfer of a particularmedia data based, at least in part, on the type of metadata (e.g., allthe different types of metadata contemplated herein) compiled andrecorded in the blockchain. In these embodiments, a processor mayanalyze the various metadata types recorded in the blockchain anddetermine if the media data adheres to one or more rules associated withthe transfer of the media data.

For example, if a processor determines that there is missing metadata ormetadata has been kept private (e.g., due to the participant's manuallyselected rules) a processor may prevent the transfer of the particularmedia data. In another example, one of the rules may includerestrictions that prevent the transfer of media data when the level ofmodification exceeds a threshold level. In this example, a heavilydoctored media data would not be able to be transferred while anotherversion of the media data with a slight retouch may be allowed to betransferred. In some embodiments, a processor may send an alertnotification to the participant initiating the transfer indicating thatthe transfer has failed. In some embodiments, if the participantattempting to send the failed transfer is the owner of the media data,the participant owner may be able to alter the one or more rulesassociated with the transfer and initiate the transfer again.

In embodiments, a processor may enforce (e.g., via the AI engine) theone or more rules associated with the governance system to determinewhen one or more rules may have been violated by a participant. In someembodiments, a processor may prevent a participant from violating theone or more rules by interacting with the device or software theparticipant is using to prevent the modification from occurring or beingperformed on the media data. In some embodiments, in addition topreventing the modification from occurring, a processor may configurethe governance system to issue an alert notification each time aparticipant attempts to perform a modification that violates the one ormore rules. In some embodiments, a processor (e.g., via the governancesystem) may send the alert notification to the participant who isviolating the one or more rules and/or may send an alert notification tothe owner(s) of the media data. In these embodiments, the alertnotification may notify the participant of the rule(s) they areattempting to violate. In some embodiments, an alert notification issuedto a participant may provide additional information. For example, analert notification may include additional information stating that inorder to perform a particular modification, the owner of the media datamust first approve the particular modification. In this example, theadditional information may include how such approval may be obtainedfrom the owner.

In embodiments, a processor may configure the governances system toaudit the participant network to determine if one or more rules havebeen violated. In some embodiments, this audit may be performed usingthe AI engine. In these embodiments, the AI engine may analyze thevarious media data generated by the participants' activities andinvolvement in the participant network. In such embodiments, a processormay analyze the information and data collected (e.g., metadata,additional metadata, violation metadata, etc.) from the participantnetwork and identify if a participant has performed any actionsassociated with the of media data that may constitute a rule violation.In one example embodiment, a processor may identify that an image, ownedby one participant, has plagiarized another participant and/or the imagehas been modified in such a manner as to violate the one or more rules.In such embodiments, a processor (e.g., via the governance system) mayperform a dispute resolution analysis to aid in resolving the identifiedrule violations. For example, a dispute resolution analysis may be usedto aid in the identification of the participant with the valid ownershiprights by analyzing when the media data was generated and which imagewas generate/created first using the metadata and additional metadatacollected and recorded in the blockchain.

In embodiments, a processor may collect and compile metadata associatedwith the rule violation, or violation metadata. Violation metadata mayinclude, but is not limited to, metadata associated with the particularparticipant who violated the rule, how the rule was violated (e.g., whatalteration was made to the media data that violated the one or morerules), how often the particular participant has had a rule violation,and/or what actions were taken to mitigate harm or damages associatedwith the violation. For example, if a participant generated a deep fakethat exchanged the audio of a person giving a benign speech withoffensive audio that violated the one or more rules, a processor (e.g.,via the governance system) could be configured to permanently delete thedeep fake.

In some embodiments, a processor may also collect violation metadatathat includes what other participants, within the participant network,viewed the modified media data (e.g., nefarious deep fake). Inembodiments where a deep fake or other modified media data violates oneor more rules, a processor (e.g., via AI engine of governance system)may send an alert notification to each of the viewers indicating thatthe media data they saw was modified in such a manner that one or morerules were violated (e.g., with the intent to deceive). In someembodiments, the alert notification may include a link or copy of themedia data in its unmodified form (e.g., original or modified versionprior to the violation of the rules). Such embodiments may reduce thelegitimacy of false narratives spread as factual information. Inembodiments, violation metadata could be collected and incorporated intothe blockchain to maintain a record of what rule violations of the oneor more rules occurred and what participants were associated with theone or more rule violations.

In embodiments, as contemplated herein a processor (e.g., via governancesystem) may generate the one or more rules using a variety of methods.In some embodiments, a processor may utilize an AI rule engine togenerate one or more rules. While in some embodiments, the AI ruleengine is a subcomponent of the AI engine (e.g., The AI engine iftrained to perform AI rule assignment) enforcing the one or more rulesof the governance system, in other embodiments, the AI rule engine is aseparately trained AI system. In embodiments, the AI rule engine mayanalyze historical data of the participant network, such as the varioustypes of metadata and information compiled within the blockchain, togenerate the one or more rules. In these embodiments, a processor maytask the AI rule engine to identify one or more rules that mayaccomplish one or more system goals. For example, a processor could taskthe AI rule engine to develop rules that may prevent and/or limit theexposure of nefarious deep fakes throughout the participant network. Inembodiments, the AI rule engine may produce one or more rules associatedwith particular participants, subsets of participants, all of theparticipants, or any combination thereof. While in some embodiments, aparticipant who generated or created the media data (e.g., owner of themedia data) may be able define one or more rules that should be appliedto their particular media data, in other embodiments, the AI rule enginemay automatically generate a selection of rules that a participant/ownermay select and customize the one or more rules that apply to the mediadata. For example, the AI rule engine could automatically generate RuleA, Rule B, and Rule C for a particular media data (e.g., image) and theparticipant/owner could select Rule A and Rule C to apply to theirparticular media data.

While embodiments discussed above provide an overview of managingmodifications of media data, the following provides not only additionalembodiments but also demonstrates additional features and variationsregarding the aforementioned embodiments.

In embodiments, while a participant may include one or more individuals,in other embodiments, a participant may also include companies, businessnetworks, enterprises, organizations, or any combination thereof. Insome embodiments, a participant may include an enterprise/companyengages in the manufacture and/or development of devices (e.g., mobilephones, cameras, laptops, etc.) that may be used modify media data. Inthese embodiments, users of such devices may be automaticallyincorporated into the participant network as a participant uponpurchasing and/or using the device. For example, a device may require aparticipant (e.g., user of a device) to enter and/or create a loginprior to using the device. This use of a login may incorporate the userof a device as a participant of the participant network.

In some embodiments, a participant may include enterprises/companiesthat develop software and/or applications that may enable a user toperform modification of media data (e.g., Adobe Photoshop®, TechSmithCamtasia®, Apple QuickTime®, Microsoft Office Suite®, etc.). In theseembodiments, users who use such software and/or applications may beautomatically incorporated into the participant network as aparticipant. For example, a user of a particular application thatenables a user to modify media data may be required to enter and/orcreate a login to the application in order to perform such actions. Thisuse of a login may incorporate the user of the software/application as aparticipant of the participant network.

In some embodiments, companies (e.g., participants) that developsoftware/application in areas which may involve indirect modification ofmedia data may also be added to the plurality of devices of theparticipant network. Such software that could indirectly modify mediadata may include, but is not limited to, software tools for datawarehousing, data science, data analytics, business intelligence, andmachine learning (e.g., IBM Watson Studio®, IBM Cloud Pak for Data®, IBMDB2®, Azure Data Studio®, Google Refine®, TensorFlow®, IBM Watson®,Mockaroo®, etc.)

For example, in some embodiments, a participant who is a company, abusiness network, an enterprise, and/or an organization may beconsidered a participant and each individual user (e.g., ofsoftware/application and/or device) may also be a participant to theparticipant network. In these embodiments, a company, business network,enterprise, and/or organization participant may pre-build and/or embedtechniques contemplated herein within the software/applications and/ordevices. In some embodiments, a company, business network, enterprise,and/or organization participant may generate one or more rules that mayautomatically apply to the users (e.g., participants) of thesoftware/applications and/or devices. In some embodiments, a company,business network, enterprise, and/or organization participant maygenerate a menu of one or more rules that may selected by participantusers to apply (e.g., manage the modification) to media data that theparticipant user has captured or created. In some embodiments, theparticipant user of the software/applications and/or devices maygenerate their own one or more rules. In some embodiments, the AI ruleengine may be configured to generate and/or recommend one or more rulesfor all participants or less than all participants who use thesoftware/applications and/or devices developed/manufactured by acompany, business network, enterprise, and/or organization participant.

As contemplated herein, to aid in the management of modifying mediadata, various data and metadata is collected (e.g., metadata, additionalmetadata, violation metadata, etc.). In embodiments, a participant usermay limit or restrict the information and details (e.g., metadata)available to the company, business network, enterprise, and/ororganization participant. In embodiments, a participant (e.g.,participant user) may configure one or more preferences insoftware/application and/or device. While the participant may have theability to restrict some information, media data created or modified(e.g., altered, doctored, modified, etc.) where specific details arerestricted and not shared with the participant network may have alimited use. In embodiments, this may be based on how the company,business network, enterprise, and/or organization participant determinesthe one or more rules will apply to their users.

In embodiments, as contemplated herein, the participant network may beenhanced with an AI engine (e.g., IBM Watson®) to enforce the one ormore rules of the governance system. In these embodiments, thegovernance system may be enabled with various AI and ML algorithms thatmay be trained with industry models (and others) to identify/catch andrestrict the modification, particularly malicious and negative use, ofmedia data in its various forms (e.g., deep fakes).

In embodiments, company, business network, enterprise, and/ororganization participant may be able to define one or more rules fordifferent types of software/applications and/or devices that may beconfigured to create, interact with, or handle different forms of mediadata. In embodiments, the one or more rules define how a media data maybe doctored, altered, or modified (e.g., modified). In one exampleembodiments, media data, such as an image, may be created via a device,such as the camera manufactured in a mobile phone. In this exampleembodiment, the participant users may have the flexibility to restrictthe information shared as metadata to the blockchain. In another exampleembodiment, a participant user may modify an existing media data (e.g.,an image) using another device, such as a laptop. In these exampleembodiments, one or more different rules may apply to the particularscenario depending on the actions of the participant user.

In embodiments, as contemplated herein, one or more rules may beassigned to one or more participants of the participant network based,at least in part, on an AI rule engine (e.g., as part of the governancesystem). In embodiments, the AI rule engine may automatically generateone or more rules when one or more media data is created/generated(e.g., via a camera in a mobile phone). In embodiments, the one or morerules may be enforced by the AI engine of the governance system (asdiscussed herein). In these embodiments, the AI engine may use theinformation and data details incorporated into the blockchain (e.g.,single point of truth) to enforce the one or more rules. While in someembodiments, a participant may be forced to comply with the one or morerules generated by the AI rule engine, in other embodiments, the one ormore rules generated by the AI rule engine may be redefined and/oraltered by the participant (e.g., either a participant user or aparticipant organization). In embodiments, this capability may beavailable to all data owners (e.g., participants) uniformly throughoutthe participant network, regardless of the use of differentsoftware/applications and/or devices used. For example, a participantuser may use a mobile phone with a camera to capture a picture (e.g.,media data). In this example, the one or more rules may be set todefault (e.g., configured by the company that manufactures the mobilephone) during the creation process (e.g., taking the picture).Continuing this example when the participant user uploads the picture toa software/application program to modify the image (e.g., alter, modify,doctor, etc.) the governance system (e.g., using AI engine) may indicatethat via the one or more default rules the image cannot be altered. Butafter analysis, the governance system may identify the participant useras the owner of the media data and allow the participant user to bypassthe one or more rules restricting modification of the image. In someembodiments, the software/application may automatically identify theparticipant as the owner/creator of the media data. While in some ofthese embodiments, a processor may automatically update the one or morerules, in other embodiments the participant may alter the one or morerules (e.g., because they are the owner of the media data) and continuemodifying the media data in the software/application. In embodimentswhere the participant alters the one or more rules, such information(e.g., metadata) is automatically updated and incorporated into theblockchain to ensure the most recent modification rules and permissionsare recorded.

In embodiments, as contemplated herein, the AI rule engine may usehistorical data that includes system insights (e.g., specific tasks ormodification process, participant details, past activity etc.). Inembodiments, the AI rule engine may provide a seamless data modificationprocess for participants of the participant network (e.g., media dataowners, permitted users of media data, etc.). This seamless datamodification process may be performed irrespective of the format of themedia data (e.g., image, video, multimedia, etc.) regardless of wherethe modification process is performed, on a device or usingsoftware/application. In embodiments, the AI rule engine may beavailable (e.g., as a default) to participants that have a claim toownership of the media data. In these embodiments, a participant may beable to manage the AI rule engine using an interface (e.g., of thesoftware or device) that may interact with the media data.

In some embodiments, the AI rule engine may be continuously active andmay dynamically define or alter the one or more rules associated with aparticular media data. In some embodiments, the AI rule engine maydynamically alter the one or more rules when the participant (e.g.,owner or permitted user of media data) performs a particularmodification task on a particular media data. In one example embodiment,a participant who is the owner of an image, having no defined rules, maymodify the image on their mobile phone. In this example embodiment, aprocessor (e.g., via governance system) may automatically accessmetadata from the participant such as, user details, device details,software details and other relevant data, that may be incorporated intothe blockchain. In this example embodiment, the AI rule engine mayautomatically assign one or more rules associated with the particularmodification on the mobile phone. If the participant were to decide toopen the media data on a different application, the processor may stillaccess the relevant metadata (e.g., any metadata as discussed herein)and the processor (e.g., via the AI engine and/or AI rule engine of thegovernance system) may enforce the one or more rules associated with themedia data. In some embodiments, the generation of one or more rules maybe performed on the backend of the operating system. Such embodimentsmay provide for the seamless modification of media data for the one ormore participants of the participant network.

In some embodiments, a processor may configure the AI rule engine toassign one or more rules associated with managing media data may applyto individual participants, team participants, and/or to particulardevices and/or software/applications. Such rules may be managed andenforced by the participant network (e.g., AI engine of the governancesystem). For example, a video presentation (e.g., media data) may beassigned one or more rules including, but not limited to, restrictingthe permission to modify the video presentation to a particularindividual participant (e.g., a participant with a particular employeenumber), a specific group of participants performing a common function(e.g., the development team associated with cloud management), a groupof participants having a specific permission level (e.g., companyexecutives may perform modification), restricting the modification toonly be performed on one device (e.g., identified with a serial number)and/or a particular type/group of device (e.g., mobile phone only, agroup mobile phones with specific serial number's), restricting themodification to only be performed using a particularsoftware/application program.

In some embodiments, a processor may assign one or more rules based oncontracts available between participants of the participant network. Forexample, a marketing company participant may agree to represent aparticular product (e.g., footwear) produced by the footwear companyparticipant to market and generate awareness of the product. Theagreement may include particular legal and business conditions that needto be addressed by each footwear company participant and the marketingcompany participant. In embodiments, a processor may use the AI engineof the governance system to generate one or more rules based, at leastin part, on the contract or agreement between participants of theparticipant network. In these embodiments, by using the AI engine togenerate the one or more rules based off of the contracts or agreementsbetween participants, a processor may ensure that any modificationperformed on the media data is within the contractual terms of theagreement and may restrict any modification of media data that mayresult in a breach of contract or misuse of the media data of interest.Continuing the above example, a video of a famous athlete wearing a shoedesigned by the footwear company participant cannot be modified by themarketing company participant to show falsely that the shoe makes thefamous athlete faster if the contract included conditions associatedwith false representation of the product.

In some embodiments, a processor may limit the availability of mediadata based on the location of the participant. In some embodiments, thislocation based restriction may be based on a participant owner manuallyinputting the location restriction as a rule in the one or more rules.In other embodiments, such location restrictions may be based at leastin part on conditions identified in a contract. For example, a contractbetween participant of the participant network may limit theavailability media data and/or the type of modification processing thatcan be performed on the media data to a particular geographical area.This may be due to local copyright laws that differ depending on thecountry the participants may be conducting business in.

In embodiments, a processor may use the AI engine of the governancesystem to regulate and ensure the secure import/export or transfer ofmedia data. In scenarios where a participant is attempting to importmedia data into storage software, data science and analytics tools,and/or any other tool or product that may handle media data, a processormay use the AI engine to determine if such actions violate the one ormore rules. In such embodiments, a processor may issue one or more alertnotification if the media data is being transferred to a location thatviolates one or more rules. For example, if a participant is attemptingto transfer the media data to a party that is not part of theparticipant network, an alert notification may be issued and theparticipant who is attempting to transfer the media data may beprevented from performing such actions. In some embodiments, media datathat is attempted to be transferred in such a manner as to violate theone or more rules may be filtered into a security vulnerable list ortable. In some embodiments, an alert notification may include additionalinformation associated with how and where the participant may transfererthe media data and what features are available to the participantregarding the particular media data. For example, the additional how themedia data can be modified and where specifically the media data may betransferred or stored.

In some embodiments, a processor may interact with media platforms(e.g., news outlets) and/or social media platforms (e.g., Facebook®,Twitter®, etc.) to provide authenticity results. In embodiments, aprocessor may be configured to generate a validation summary associatedwith a particular media data that may be used to prove and/or supportauthenticity. For example, some media data may not have one or morerules managing how the media data may be modified. In these scenarios,participants with access to the media data may be able to alter/modifythe media data in any number of ways without violating any of the one ormore rules. For example, a participant could generate a nefarious deepfake of a presentation using one or more particular media datacomponents without violating any of the one or more rules. However,because the participant(s) are performing the actions resulting in themodification of the media data within the participant network, eachaction is maintained in the blockchain ledger. As such, in someembodiments, when a participant attempts to upload a media data to amedia platform or social media platform, a processor may access theinformation incorporated in the blockchain (e.g., any metadatacontemplated herein, such as metadata, additional metadata, violationmetadata, etc.) and generate a validation summary that includes, atleast, what modifications have been performed on the media data in itsoriginal form. The information in the validation summary may beavailable to all of the viewers, whether they are part of theparticipant network or not.

In embodiments, a processor may be configured to work with a socialmedia platform. In some embodiments, if a participant restricts some orall of the various types of metadata from being recorded andincorporated into the blockchain, the participant will be prevented fromuploading the media data to the social media platform. In otherembodiments, the social media platform may restrict the media datauploaded to the platform and may prevent the participant from postingthe video to the social media platform or limit how the media data isshared on the platform.

In embodiments, as contemplated herein, a company participant may beable to secure and ensure the veracity of media data and content basedon the one or more rules (e.g., associated with managing modification,security). The one or more rules may be defined by an AI rule engineand/or manually defined by the company participant. For example, anoffering team participant may be able to define the rules ofmodification associated with a spreadsheet of data (e.g., media data) torestrict based on a client's rules and make recommendations based on theclient's personal preferences and past activity. In embodiments, thismay include a warning (e.g., an alert notification) when a clientparticipant is attempting to clear one or more datasets in thespreadsheet that violates the one or more rules (e.g., the clientparticipant does not have permission to delete/clear the one or moredatasets in the spreadsheet).

In one example embodiments, a participant of a participant network mayrecord an original video of an interview with a particular device. Oncethe interview if finished the interview video is available on theparticular device. In such embodiments, a processor may automaticallycollect metadata including, but not limited to, the interview videoidentifier, the particular device type (e.g., type of camera), userdetails (e.g., participant name), modification type (e.g., original oruntouched), location (e.g., United States), Date captured, and one ormore rules (e.g., default rules). This metadata may be recorded andincorporated into a blockchain associated with the participant network.In this example embodiment, the participant may decide to edit/modifythe original interview video by importing the interview video tosoftware/applications having modification capabilities. In embodiments,the participant may merge various marketing clips into the originalinterview video using the aforementioned software/application. In thisexample embodiment, the processor may automatically provide thesoftware/application with the metadata previously collected by theprocessor.

The processor may automatically trigger alert notifications associatedwith the software/application. For example, if the metadata does notinclude who the owner of the interview video is, a processor may send analert notification (e.g., pop-up message) indicating that one of thedefault rules is that only those with permission from the owner of themedia data are able to modify the interview video. In embodiments, aprocessor may provide the participant with an ability to demonstratethat they are the owner. If the processor determines the participant isthe owner of the interview video or has obtained the right toedit/modify the interview video, the processor may provide theparticipant with the option to bypass the rules and/or customize one ormore rules. In some embodiments, the one or more rules may be a genericset of rules, while in other embodiments, the AI rule engine maygenerate the initial set of default rules. If the processor determinesthe participant is not the owner and has not obtained the right toedit/modify the interview video, the processor will prevent theparticipant from performing the planned modifications (e.g., mergingmarketing clips into the interview video).

In this example embodiment, if a participant decides to customize theone or more rules, a participant may select one or more rules that mayrestrict participants who have permission to perform the modification ofthe media data. For example, a participant may include, but is notlimited to, one or more rules that limits the individuals who canperform modifications on the media data, rules that limit the devicesand/or software/applications that may preform modifications on the mediadata, a specific group of individuals (e.g., anyone belonging to aparticular group or team), a specific group of devices (e.g., onlydevices located at a particular location) may perform the modifications,or any combination thereof.

In embodiments, a processor may record the customized rules to theblockchain associated with the participant network. In theseembodiments, the blockchain is continuously updated with the most recentinformation to ensure the blockchain maintains an accurate record ofinformation. In embodiments, the software/application and/or device maybe configured to have access to the various forms of metadata from theparticipant network. Continuing the above example embodiments, inembodiments where a participant is the owner of the media data anddecides to customize the one or more rules, the software/applicationand/or device may receive this updated set of one or more rules andallow the participant (e.g., the owner) to perform the modifications. Insome embodiments, a processor may configure the software/application toprovide one or more recommendations associated with the modification ofthe media data. In these embodiments, the processor may base the one ormore recommendations on the participants personal preferences, pastactivity, employer regulations, and/or employer security policies. Forexample, using the above example embodiments, a processor may recommendthe use of particular marketing clips in the editing/modification of theinterview video. In such embodiments, a processor may analyze and/ordetermine (e.g., via AI engine) if the participant has at any point(e.g., during the process of performing one or more modifications)violated one or more rules associated with the governance system. Inembodiments, a processor may be configured to save the modified mediadata in a cloud storage system. While in some embodiments, a processormay configure a storage system where all participants may store and/orview media data associated with the participant network, in otherembodiments, a processor may interact with other storage systems notdirectly associated with the participant network (e.g., cloud basedstorage systems, hard disk storage, etc.). In these embodiments, aprocessor may interact with the storage system (e.g., on the backend)and provide the storage system with access to the various forms ofmetadata (e.g., metadata incorporated in the blockchain) associated withthe particular media data attempting to be saved. In these embodiments,when media data is saved alert notifications associated with theviolation of one or more rules may still be issued.

In some embodiments, a processor may also use the AI engine to performAI-driven production/modification of media data. In these embodiments, aprocessor may use the AI engine to derive insights and recommendationsbased, at least in part, on the participant's needs, such as theparticipant's need to satisfy a business client (e.g., based off ofindustry standards and target offerings, etc.). In embodiments, the AIengine may be configured to use text synthesis, natural languageprocessing (NLP), deepfake/video synthesis, audio synthesis, generativeadversarial networks, autoencoders, and any other processing or machinelearning techniques that may aid in such recommendation processes. Insome embodiments, a processor, via the AI engine, may automaticallygenerate a modified media data that can be used by the participant for aparticular purpose, such as for a marketing campaign. For example, aprocessor using the AI engine may compile historical data, participantmetadata, and participant insights to modify a video commercial inEnglish and modify the video commercial to be used in a differentcountry. For example, if the video commercial has an English speakingathlete promoting a type of footwear, the AI engine may modify the videocommercial in such a way as the English speaking athlete nowconvincingly appears to be speaking German in a video commercial thatwill be used to advertise the footwear to people in Germany.

Referring now to FIG. 1 , a block diagram of an media modificationmanagement system 100 for managing media modification within aparticipant network, is depicted in accordance with embodiments of thepresent disclosure. FIG. 1 provides an illustration of only oneimplementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environment may be made by those skilledin the art without departing from the scope of the invention as recitedby the claims. In some embodiments, the solar panels may be directed bythe computing device to move to a more advantageous angle.

In embodiments, media modification management system 100 may includeparticipant network 102, governance system 104, and an immutabletracking component 106. In embodiments, participant network 102 (e.g.,business network) may include any number of participant(s) 108 anddevices 110 as contemplated herein, that may contribute to themodification or production of media data. In some embodiments mediamodification management system 100 may also include enterprises 116.While in some embodiments, enterprises 116 may be considered aparticipant, in other embodiments, an enterprise 116 may be separatefrom the participants. In some embodiments, an enterprise 116 mayinclude software 118 and applications 120.

In embodiments, governance system 104 may govern or enforce theparticipant network to ensure that only authorized modification isperformed on the media data and that restricted modification of mediadata is prevented from occurring. In some embodiments, governance system104 may include an AI engine 112. In these embodiments, AI engine 112may analyze the participant network and perform one or more enforcementfunctions to manage the modification of media data. In some embodiments,AI engine 112 may also include an AI rule engine that may use AI andmachine learning to generate one or more rules 114 associated with themedia data. In these embodiments, one or more rule 114 may includestandards associated with managing, restricting and/or enablingparticular modifications of media data. In some embodiments, rule(s) 114may be determined independently of AI engine 112. In these embodiments,the owner of the media data may select and determine what modificationsmay or may not be performed on the media data they own.

In embodiments, immutable tracking component 106 may act as a ledger totrack users or machine (device or software or application) interactionswith various media data. In some embodiments, the immutable trackingcomponent 106 may be configured as a blockchain network. For example,immutable tracking component 106 may be configured as a HyperledgerFabric blockchain network. Immutable tracking component 106 may be usednot only to identify the owner of a particular media data, but also toidentify what user or machine (device or software or application) hasperformed a particular modification.

Referring now to FIG. 2 , a flowchart illustrating an example method 200for managing media modification within a participant network, inaccordance with embodiments of the present disclosure. FIG. 2 providesan illustration of only one implementation and does not imply anylimitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironment may be made by those skilled in the art without departingfrom the scope of the invention as recited by the claims.

In embodiments, the method 200 begins at operation 202 where a processormay generate a participant network having a plurality of devices. Inembodiments, the plurality of devices may be associated with one or moremedia data and a user. In some embodiments, the method 200 proceeds tooperation 204.

At operation 204, a processor may enforce a governance system on theparticipant network. In some embodiments, the governance system mayinclude at least one rule to manage modification of the one or moremedia data by the user. In some embodiments, the method 200 may proceedto operation 206.

At operation 206, a processor may restrict the user from modifying theone or more media data. In embodiments, this restriction may be based,at least in part, on the governance system. In some embodiments, asdepicted in FIG. 2 , after operation 206, the method 200 may end.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of portion independence in that the consumergenerally has no control or knowledge over the exact portion of theprovided resources but may be able to specify portion at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 3A, illustrative cloud computing environment 310is depicted. As shown, cloud computing environment 310 includes one ormore cloud computing nodes 300 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 300A, desktop computer 300B, laptop computer300C, and/or automobile computer system 300N may communicate. Nodes 300may communicate with one another. They may be grouped (not shown)physically or virtually, in one or more networks, such as Private,Community, Public, or Hybrid clouds as described hereinabove, or acombination thereof. This allows cloud computing environment 310 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 300A-Nshown in FIG. 3A are intended to be illustrative only and that computingnodes 300 and cloud computing 300 and cloud computing environment 310can communicate with any type of computerized device over any type ofnetwork and/or network addressable connection (e.g., using a webbrowser).

Referring now to FIG. 3B, a set of functional abstraction layersprovided by cloud computing environment 310 (FIG. 3A) is shown. Itshould be understood in advance that the components, layers, andfunctions shown in FIG. 3B are intended to be illustrative only andembodiments of the disclosure are not limited thereto. As depictedbelow, the following layers and corresponding functions are provided.

Hardware and software layer 315 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 302;RISC (Reduced Instruction Set Computer) architecture based servers 304;servers 306; blade servers 308; storage devices 311; and networks andnetworking components 312. In some embodiments, software componentsinclude network application server software 314 and database software316.

Virtualization layer 320 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers322; virtual storage 324; virtual networks 326, including virtualprivate networks; virtual applications and operating systems 328; andvirtual clients 330.

In one example, management layer 340 may provide the functions describedbelow. Resource provisioning 342 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 344provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 346 provides access to the cloud computing environment forconsumers and system administrators. Service level management 348provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 350 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 360 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 362; software development and lifecycle management 364;virtual classroom education delivery 366; data analytics processing 368;transaction processing 370; and media data managing 372.

FIG. 4 , illustrated is a high-level block diagram of an examplecomputer system 401 that may be used in implementing one or more of themethods, tools, and modules, and any related functions, described herein(e.g., using one or more processor circuits or computer processors ofthe computer), in accordance with embodiments of the present invention.In some embodiments, the major components of the computer system 401 maycomprise one or more Processor 402, a memory subsystem 404, a terminalinterface 412, a storage interface 416, an I/O (Input/Output) deviceinterface 414, and a network interface 418, all of which may becommunicatively coupled, directly or indirectly, for inter-componentcommunication via a memory bus 403, an I/O bus 408, and an I/O businterface unit 410.

The computer system 401 may contain one or more general-purposeprogrammable central processing units (CPUs) 402A, 402B, 402C, and 402D,herein generically referred to as the CPU 402. In some embodiments, thecomputer system 401 may contain multiple processors typical of arelatively large system; however, in other embodiments the computersystem 401 may alternatively be a single CPU system. Each CPU 402 mayexecute instructions stored in the memory subsystem 404 and may includeone or more levels of on-board cache.

System memory 404 may include computer system readable media in the formof volatile memory, such as random access memory (RAM) 422 or cachememory 424. Computer system 401 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 426 can be provided forreading from and writing to a non-removable, non-volatile magneticmedia, such as a “hard drive.” Although not shown, a magnetic disk drivefor reading from and writing to a removable, non-volatile magnetic disk(e.g., a “floppy disk”), or an optical disk drive for reading from orwriting to a removable, non-volatile optical disc such as a CD-ROM,DVD-ROM or other optical media can be provided. In addition, memory 404can include flash memory, e.g., a flash memory stick drive or a flashdrive. Memory devices can be connected to memory bus 403 by one or moredata media interfaces. The memory 404 may include at least one programproduct having a set (e.g., at least one) of program modules that areconfigured to carry out the functions of various embodiments.

One or more programs/utilities 428, each having at least one set ofprogram modules 430 may be stored in memory 404. The programs/utilities428 may include a hypervisor (also referred to as a virtual machinemonitor), one or more operating systems, one or more applicationprograms, other program modules, and program data. Each of the operatingsystems, one or more application programs, other program modules, andprogram data or some combination thereof, may include an implementationof a networking environment. Programs 428 and/or program modules 430generally perform the functions or methodologies of various embodiments.

Although the memory bus 403 is shown in FIG. 4 as a single bus structureproviding a direct communication path among the CPUs 402, the memorysubsystem 404, and the I/O bus interface 410, the memory bus 403 may, insome embodiments, include multiple different buses or communicationpaths, which may be arranged in any of various forms, such aspoint-to-point links in hierarchical, star or web configurations,multiple hierarchical buses, parallel and redundant paths, or any otherappropriate type of configuration. Furthermore, while the I/O businterface 410 and the I/O bus 408 are shown as single respective units,the computer system 401 may, in some embodiments, contain multiple I/Obus interface units 410, multiple I/O buses 408, or both. Further, whilemultiple I/O interface units are shown, which separate the I/O bus 408from various communications paths running to the various I/O devices, inother embodiments some or all of the I/O devices may be connecteddirectly to one or more system I/O buses.

In some embodiments, the computer system 401 may be a multi-usermainframe computer system, a single-user system, or a server computer orsimilar device that has little or no direct user interface, but receivesrequests from other computer systems (clients). Further, in someembodiments, the computer system 401 may be implemented as a desktopcomputer, portable computer, laptop or notebook computer, tabletcomputer, pocket computer, telephone, smartphone, network switches orrouters, or any other appropriate type of electronic device.

It is noted that FIG. 4 is intended to depict the representative majorcomponents of an exemplary computer system 401. In some embodiments,however, individual components may have greater or lesser complexitythan as represented in FIG. 4 , components other than or in addition tothose shown in FIG. 4 may be present, and the number, type, andconfiguration of such components may vary.

As discussed in more detail herein, it is contemplated that some or allof the operations of some of the embodiments of methods described hereinmay be performed in alternative orders or may not be performed at all;furthermore, multiple operations may occur at the same time or as aninternal part of a larger process.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, executed concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be executed in thereverse order, depending upon the functionality involved. It will alsobe noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

Although the present invention has been described in terms of specificembodiments, it is anticipated that alterations and modification thereofwill become apparent to the skilled in the art. Therefore, it isintended that the following claims be interpreted as covering all suchalterations and modifications as fall within the true spirit and scopeof the disclosure.

1. A method for managing media modification within a participantnetwork, the method comprising: generating, by a processor, theparticipant network having a participant, wherein the participant isassociated with one or more media data; enforcing one or more rules onthe participant network using an artificial intelligence (AI) basedgovernance system, wherein the one or more rules manage the modificationof the one or more media data by the participant; analyzing the one ormore media data for authenticity, based on the one or more rules;determining one or more authenticity results of the one or more mediadata having one or more modifications; and restricting the participantfrom modifying the one or more media data associated with a particularmodification based, at least in part, on the AI based governance system.2. The method of claim 1, wherein the participant network includes:archiving one or more actions associated with the participant and theone or more media using an immutable tracking component.
 3. The methodof claim 1, further comprising: automatically generating the one or morerules based, at least in part, on an AI rule engine.
 4. The method ofclaim 1, further comprising: analyzing a modification of the one or moremedia data performed by a participant; and determining the modificationof the one or more media data violates the one or more rules.
 5. Themethod of claim 4, further comprising: identifying the participant as anowner of the one or more media data; and bypassing the one or more rulesof the AI based governance system, wherein bypassing the one or morerules allows the owner to perform the modification.
 6. The method ofclaim 4, further comprising: generating an alert notification that theone or more rules has been violated by the participant.
 7. The method ofclaim 1, further comprising: generating an alert when the participantimports the one or more media data to an external system.
 8. A systemfor managing media modification within a participant network, the systemcomprising: a memory; and a processor in communication with the memory,the processor being configured to perform operations comprising:generating the participant network having a participant, wherein theparticipant is associated with one or more media data; enforcing one ormore rules on the participant network using an AI based governancesystem, wherein the one or more rules manage the modification of the oneor more media data by the participant; analyzing the one or more mediadata for authenticity, based on the one or more rules; determining,responsive to analyzing the one or more media data for authenticity, oneor more authenticity results associated with the one or more media datahaving one or more modifications; and restricting the participant frommodifying the one or more media data associated with a particularmodification based, at least in part, on the AI based governance system.9. The system of claim 8, wherein the participant network includes:archiving one or more actions associated with the participant and theone or more media using an immutable tracking component.
 10. The systemof claim 8, further comprising: automatically generating the one or morerules based, at least in part, on an AI rule engine.
 11. The system ofclaim 8, further comprising: analyzing a modification of the one or moremedia data performed by a participant; and determining the modificationof the one or more media data violates the one or more rules.
 12. Thesystem of claim 11, further comprising: identifying the participant asan owner of the one or more media data; and bypassing the one or morerules of the AI based governance system, wherein bypassing the one ormore rules allows the owner to perform the modification.
 13. The systemof claim 11, further comprising: generating an alert that the one ormore rules has been violated by the participant.
 14. The system of claim8, further comprising: generating an alert when the participant importsthe one or more media data to an external system.
 15. A computer programproduct for managing media modification within a participant network,the computer program product comprising a computer readable storagemedium having program instructions embodied therewith, the programinstructions executable by a processor to cause the processors toperform a function, the function comprising: generating the participantnetwork having a participant, wherein the participant is associated withone or more media data; enforcing one or more rules on the participantnetwork using an AI based governance system, wherein the one or morerules manage the modification of the one or more media data by theparticipant; analyzing the one or more media data for authenticity,based on the one or more rules; determining, responsive to analyzing theone or more media data for authenticity, one or more authenticityresults associated with the one or more media data having one or moremodifications; and restricting the participant from modifying the one ormore media data associated with a particular modification based, atleast in part, on the AI based governance system.
 16. The computerprogram product of claim 15, wherein the participant network includes:archiving one or more actions associated with the participant and theone or more media using an immutable tracking component.
 17. Thecomputer program product of claim 15, further comprising: automaticallygenerating the one or more rules based, at least in part, on an AI ruleengine.
 18. The computer program product of claim 15, furthercomprising: analyzing a modification of the one or more media dataperformed by a participant; and determining the modification of the oneor more media data violates the one or more rules.
 19. The computerprogram product of claim 18, further comprising: identifying theparticipant as an owner of the one or more media data; and bypassing theone or more rules of the AI based governance system, wherein bypassingthe one or more rules allows the owner to perform the modification. 20.The computer program product of claim 18, further comprising: generatingan alert that the one or more rules has been violated by theparticipant.